Background The multidisciplinary nature of nutrition research is one of its main strengths. At the same time, however, it presents a major obstacle to integrate data analysis, especially for the terminological and semantic interpretations that specific research fields or communities are used to. To date, a proper ontology to structure and formalize the concepts used for the description of nutritional studies is still lacking. Results We have developed the Ontology for Nutritional Studies (ONS) by harmonizing selected pre-existing de facto ontologies with novel health and nutritional terminology classifications. The ONS is the result of a scholarly consensus of 51 research centers in nine European countries. The ontology classes and relations are commonly encountered while conducting, storing, harmonizing, integrating, describing, and searching nutritional studies. The ONS facilitates the description and specification of complex nutritional studies as demonstrated with two application scenarios. Conclusions The ONS is the first systematic effort to provide a solid and extensible formal ontology framework for nutritional studies. Integration of new information can be easily achieved by the addition of extra modules (i.e., nutrigenomics, metabolomics, nutrikinetics, and quality appraisal). The ONS provides a unified and standardized terminology for nutritional studies as a resource for nutrition researchers who might not necessarily be familiar with ontologies and standardization concepts.

ONS: an ontology for a standardized description of interventions and observational studies in nutrition

Vitali et al. Genes & Nutrition
ONS: an ontology for a standardized description of interventions and observational studies in nutrition
Francesco Vitali 0 2 8
Rosario Lombardo 0 1
Damariz Rivero 8
Fulvio Mattivi 7 12
Pietro Franceschi 7
Alessandra Bordoni 6
Alessia Trimigno 6
Francesco Capozzi 6
Giovanni Felici 5
Francesco Taglino 5
Franco Miglietta 2
Nathalie De Cock 3
Carl Lachat 3
Bernard De Baets 10
Guy De Tré 9
Mariona Pinart 4
Katharina Nimptsch 4
Tobias Pischon 4
Jildau Bouwman 11
Duccio Cavalieri 2 8
the ENPADASI consortium
0 Equal contributors
1 The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI) , Piazza Manifattura, 1, I-38068 Rovereto, TN , Italy
2 Institute of Biometeorology (IBIMET), National Research Council (CNR) , Via Giovanni Caproni, 8, 50145 Florence, FI , Italy
3 Department of Food Technology, Safety and Health, Ghent University , Coupure links 653, 9000 Ghent , Belgium
4 Molecular Epidemiology Research Group, Max Delbrück Center for Molecular Medicine , Berlin , Germany
5 Institute for Systems Analysis and Computer Science (IASI), National Research Council (CNR) , Via dei Taurini, 19, 00185 Rome, RM , Italy
6 Department of Agri-Food Sciences and Technologies, University of Bologna , Piazza Goidanich 60, Cesena, FC , Italy
7 Food Quality and Nutrition Department, Research and Innovation Centre, Edmund Mach Foundation, Via Edmund Mach , 1, 38010 San Michele all'Adige, TN , Italy
8 Department of Biology, University of Florence , Via Madonna del Piano, 6, 50019 Sesto F, FI , Italy
9 Department of Telecommunications and Information Processing, Ghent University , Coupure links 653, 9000 Ghent , Belgium
10 KERMIT, Department of Data Analysis and Mathematical Modelling, Ghent University , Coupure links 653, 9000 Ghent , Belgium
11 Microbiology and Systems Biology, TNO , Utrechtseweg 48, 3704HE Zeist , The Netherlands
12 Center Agriculture Food Environment, University of Trento , San Michele all'Adige , Italy
Background: The multidisciplinary nature of nutrition research is one of its main strengths. At the same time, however, it presents a major obstacle to integrate data analysis, especially for the terminological and semantic interpretations that specific research fields or communities are used to. To date, a proper ontology to structure and formalize the concepts used for the description of nutritional studies is still lacking. Results: We have developed the Ontology for Nutritional Studies (ONS) by harmonizing selected pre-existing de facto ontologies with novel health and nutritional terminology classifications. The ONS is the result of a scholarly consensus of 51 research centers in nine European countries. The ontology classes and relations are commonly encountered while conducting, storing, harmonizing, integrating, describing, and searching nutritional studies. The ONS facilitates the description and specification of complex nutritional studies as demonstrated with two application scenarios. Conclusions: The ONS is the first systematic effort to provide a solid and extensible formal ontology framework for nutritional studies. Integration of new information can be easily achieved by the addition of extra modules (i.e., nutrigenomics, metabolomics, nutrikinetics, and quality appraisal). The ONS provides a unified and standardized terminology for nutritional studies as a resource for nutrition researchers who might not necessarily be familiar with ontologies and standardization concepts.
Ontology; Nutrition; Health; Intervention study; Observational study; Metabolomics; Food intake; Biomarker; Databases
Background
Human Nutritional Science studies the effects of food
components on metabolism, health, performance, and disease
resistance of humans, also encompassing the study of
human behavior related to food choices. Nutritional
epidemiology, on the other hand, assesses the relations
between diet, nutrients and health, and disease outcomes
[
1
]. Yet, there is a major disconnection between the
description of nutrition-based prevention of disease and the
understanding of the complex network of interactions by
which nutrition modulates health. To fill this gap, a set of
nutrition-related sub-disciplines (e.g., nutritional
biochemistry, clinical nutrition, nutritional epidemiology,
nutrigenetics, and nutrimetabolomics) provide fundamental
evidence at different levels and from different perspectives,
contributing to the expansion of nutritional science as a
more systematic and complex discipline [
2, 3
]. As nutrition
data are heterogeneous in terms of quality and nature, a
comprehensive consideration of all aspects is challenging
[4], even if substantial advance has been made to improve
the reporting of findings and the data quality [
5
] of
nutrition research [
6
], which is one of the prerequisites for
integrated analysis.
To integrate evidence, a systematic re-organization of
concept definitions is needed. Currently, concept
definitions are often derived from multiple sources, with the
drawback that slight variations can lead to misleading
interpretations [
7
]. Since in bioscience in general, and in
nutritional science in particular, the same concept can
be referred to by multiple synonymous terms,
abbreviations, or acronyms [
8
], as well as using different
languages, term classifications such as the Medical Subject
Headings (MeSH) [
9
] or the NCI Thesaurus [
10
] provide
fundamental resources. However, thesauri or controlled
vocabularies for biomedical information do not specify
relations between concepts. Although those efforts can
be used to standardize general study descriptions,
considerable advances would arise from the use of resources
that, in addition to standardizing the vocabulary, also
include connections/relations between classes, such as
ontologies, specifically tailored to the nutritional sciences.
Often biomedical researchers refer to ontologies using
the terminologies more appropriately pertaining to
“controlled vocabularies,” “thesauri” (i.e., a list, often
organized in a hierarchy or taxonomy, of concepts and their
textual descriptions), or “taxonomies” (i.e., a hierarchy
consisting of terms denoting classes linked by sub- and
super-class relations). A proper ontology, however, is
defined as a formal representation of knowledge in a
certain reality (i.e., a certain domain of knowledge), in a
way that different people—and, notably, computers—can
understand the concepts it contains and learn about the
reality that is being represented [
8, 11
]. Ontologies
consist of defined classes of entities, typically structured
within a knowledge hierarchy where concepts are
connected by standardized [12] semantic relationships (i.e.,
“is-a,” “part-of”) formally specifying knowledge relations
such as generalizations of specifications of the reality of
interest [
13
].
Open Biomedical Ontologies (OBO), established in
2001, is a platform for developing interoperable
ontologies for biomedical research [
14
]. Efforts have been
made in the agricultural field to develop
nutritionoriented ontologies focused on the description of food
components such as “the food classification and
description system” [
15
] developed by European Food Safety
Authority (EFSA). Other notable efforts in developing
food-focused ontologies were reviewed elsewhere [
16
].
Based on literature search and public ontological
repository queries (OBO Foundry searched using ONTOBEE,
and Bioportal), a single example of a nutritional
ontology was found (the Bionutrition Ontology—BNO, http://
purl.bioontology.org/ontology/BNO). The latter
represents a controlled vocabulary of nutritional terms,
without a proper annotation of terms or definition of
properties, and lacks orthogonality (i.e., no terms are
imported or refer to external ontologies). To the authors’
knowledge, a proper ontology integrating the terms
related to food description, medical science, genetics,
genomics data, and nutritional science methods for diet
and health research is not available to date. To fill this
gap, we present the Ontology for Nutritional Studies
(ONS) to facilitate the harmonization and integration of
biological samples collected using different
methodologies, referred to by differing terminologies in various
fastgrowing sub-disciplines in the dietary and health research.
The ONS was developed within the European Nutritional
Phenotype Assessment and Data Sharing Initiative
(ENPADASI) consortium [
17
], which joins scientists from 51
research centers in nine countries of Europe with the
common effort to handle and make available big nutritional data
through the open access nutritional database Data Sharing
In Nutrition (DASH-IN) [
17, 18
]. DASH-IN is a distributed
pan-European infrastructure and supports the storage of
both interventional and observational studies and provides
the tools for distributed management and search and
analysis of the data [19]. The development of this infrastructure
requires an ontology to harmonize biochemical, genetic,
clinical, and nutritional concepts typically found in
intervention and observational studies. The ontology would provide
a coherent means of data annotation and data querying over
the distributed infrastructure. Further developments of the
project led to a stronger need for a proper conceptual
framework such as the ONS that could be used by a broader
nutrition community to build upon for annotating general
nutritional studies. The ENPADASI framework gathered
researchers from different nutrition-related fields (health
sciences, biology, genetics, microbiology, agricultural
sciences, food technology, science of materials, chemistry,
metabolomics, genomics, bioinformatics, and
metagenomics) and offered the ideal milieu for creating the first
ontology in nutrition.
Methods
Terms to be included in the ONS were collected among
partners of the ENPADASI consortium, as well as from
templates for data and metadata upload into the
DASHIN databases. In compliance with the OBO Foundry
principles [
14
], the ONS has been developed to be as
follows: (i) Interoperable with other ontologies, as it has
been formalized using the latest OWL 2 Web Ontology
Language [
20
] and RDF specifications [
21
] and edited
using Protégé [
22
]; the hermit reasoner
(http://hermitreasoner.com/) was used for consistency checking. (ii)
Accessible, under the Creative Commons license (CC
BY 4.0), published on GitHub
(https://github.com/enpadasi/Ontology-for-Nutritional-Studies) and at NCBO
BioPortal (http://bioportal.bioontology.org/ontologies/
ONS). (iii) Orthogonal to other ontologies by reusing
existing terms. Besides assuring compliance with the
OBO Foundry principles, we also ensured that the ONS
followed the increasingly established FAIR principles
[
23
]. As such, the ONS is also published in the
FAIRsharing database (https://fairsharing.org/bsg-s001068).
To enhance interoperability with other ontologies,
the ONS builds on a subset of the Ontology for
Biomedical Investigations (OBI) [
24
]. The subset was
created using the ONTODOG tool [
25
] and is
composed of all terms relevant to nutritional
investigations and extended also in accordance with the
bioinformatics infrastructure of ENPADASI. Moreover,
this assured the adoption of a well-defined and widely
adopted structure for the top and mid-level classes
and principally the adherence to the Basic Formal
Ontology (BFO) [
26
] as upper ontology.
Additional relevant ontologies were used
orthogonally in the ONS as discussed in the results. To ensure
and enhance orthogonality, all terms were first
searched using the ONTOBEE [
27
] web service and
catalogued with their URIs. ONTOFOX [
28
] was then
used to import all terms with related annotations and
axioms (option includeAllAnnotations). Newly defined
terms, specific to the ONS, have been labeled with
“ONS_” followed by a 7-digit number. Terms related
to food description were also included by importing a
subset of terms from the FOODON ontology [
29
]. All
intermediate files of this development process (i.e.,
template files used for web services or imported
ontologies) were stored on GitHub repository.
Additional file 1 contains instruction on how to
browse, download, and contribute to ONS. The same
instruction is also present online at the wiki page of
the GitHub repository (https://github.com/enpadasi/
Ontology-for-Nutritional-Studies/wiki). In this
development process, terms from a number of different
ontologies were imported. Table 1 reports a summary
of the classes that were imported in the ONS
(excluding individuals) and their ontology of origin.
Results
The initial ontological curation identified a large number
of relevant terms to consider. The terms were then
https://github.com/bio-ontology-research-group/unit-ontology
either imported from existing ontologies, redefined from
existing concepts, or annotated de novo. By merging
3334 terms imported from already existing ontologies
and 100 newly defined terms, the ONS describes both
intervention and observational studies in nutrition.
Central nutritional concepts
In the ONS, relevant nutritional concepts have been
related to each other to offer a well-organized synopsis of
the knowledge in health and nutrition sciences. The ONS
harmonizes all pertinent concepts from different domains,
defining appropriate relationships and improving and
simplifying the process of conceptual organization of the
many facets of real studies. Here, we present (Fig. 1) how
diet, food, and food component concepts, which can be
considered central for an ontology aimed at effectively
assisting researchers in the standardized description of the
nutritional study they are conducting, were included,
defined, and connected in the ONS.
Diet is defined as the regular course of eating and
drinking adopted by a person or animal (ONS_0000080).
For the purpose of the nutritional community, we
further detailed the diet concept into three sub-classes: (i)
Usual diet is defined as the regular course of eating and
drinking adopted by a population in a certain
geographical area, or in a certain cultural setting, or following
certain common eating behavior. It is also intended as
the diet a person would follow without further
prescription or indications, i.e., vegetarian diet (ONS_0000083).
(ii) Prescribed diet is defined as a diet prescribed by a
physician/nutritionist to meet specific nutritional needs
of a person (ONS_0000082). (iii) Intervention diet is
defined as the diet administered during an intervention
study. It usually comprises the adoption of a certain
nutritional intervention (ERO_0000347), intended as the
prescription of consuming or not consuming certain
food, and follows a precise study design. Intervention
studies usually compare at least two subgroups of a
population, one control group receiving a null nutritional
intervention and one or more test groups receiving the
intervention (ONS_0000081).
Food component is defined as any substance that is
distributed in foodstuffs. It includes materials derived
from plants or animals, such as vitamins or minerals, as
well as environmental contaminants (CHEBI_78295,
ONS_0000073). Starting from this definition, we further
detailed the food component concept into different
subclasses: (i) Nutrient (ONS_0000077): A nutrient is a food
component used by the body for normal physiological
functions that guarantee survival and growth. It must be
supplied in adequate and defined amounts from foods
consumed within a diet. Malnutrition occurs when the
right amount of nutrient is not provided. (ii) Food
bioactive (ONS_0000076): A food bioactive is a food
component other than those needed to meet basic
human nutritional needs (nutrients). Food bioactives
modulate one or more metabolic processes, possibly
resulting in the promotion of better health. The daily
required intake for food bioactives is not established yet,
and there is no demonstration that malnutrition occurs
when the right amount is not provided. (iii)
Contaminant: Contaminant is unwanted food component that
makes the food no longer suitable for use (ONS_
0000075). (iv) Additive: Additive is a component added
to food to improve or preserve it (ONS_0000074).
Multiple definitions can be found for the food concept.
As an example, CHEBI (CHEBI:33290) defines “Any
material that can be ingested by an organism” and MESH
(MeSH D005502) defines “Any substances taken in by the
body that provides nourishment.” For the purposes of the
nutritional community, the concept of food was expanded
as food is defined as a complex matrix that is consumed
by a person through the process of eating or drinking
(ONS_0000079). Foods are bearer of the nutrients,
bioactives, and, sometimes, other food components. Food
consumption, through the meal consumption, follows a
certain dietary pattern, which define the diet. Nutrients
and bioactives contained in food can be exploited by the
human organism thanks to the process of digestion
(ONS_0000101), absorption (ONS_0000102),
metabolization (ONS_0000103), or through the intervention of the
gut microflora (OHMI_0000020). The concept of food
can be split into the following: (i) Raw food: A raw food is
an uncooked, unprocessed food that is consumed in its
natural state (ONS_0000099); (ii) Processed food: A
processed food is the result of the process of home or
industrial food preparation (ONS_0000100).
In nutritional science, biomarkers are increasingly
being used to provide objective results and to avoid
biases (e.g., reporting bias and recall bias). Three groups
of biomarkers were identified for use in nutrition science
[
30
], along with the dietary biomarker development
framework: “exposure biomarker” for dietary intake and
nutrient status, “effect biomarker” for measuring
biological effects of food components, and “susceptibility
biomarker” for assessing the effects of diet on human
health. In the ONS, we are presenting the first formal
ontology application for the biomarker class (ONS_
0000095) and its sub-classes, using the definition from
the commentary [
30
]. ONTOBEE query for the
“biomarker” returned multiple results mainly from the
Experimental Factor Ontology (EFO), all having the class
“Measurement” (EFO_0001444) as super-class (a
measurement is an information entity that is a recording of
the output of a measurement such as produced by an
instrument). However, it has to be noted that a similar
class can also be found in the Information Artifact
Ontology (IAO) named “Measurement datum” (IAO_
0000109, a measurement datum is an information
content entity that is a recording of the output of a
measurement such as produced by a device). In the ONS, the
biomarker class was defined as a sub-class of the
“Measurement datum” class (IAO_0000109) in line with the OBI
ontology, which uses the IAO class.
Integrated analysis of data and joint pooled analysis
are strongly promoted in nutrition by research funders,
though raise scientists’ concern, as the scientific interest
in the open access to nutritional data often conflicts with
the General Data Protection Regulation. When fully
achieved, integrated analysis will lead to new discoveries
and maximize use of public funds. In ENPADASI, this
problem was broadly dealt with from both legal and
technical aspects, and a recommendation on minimal
information to be added as metadata to studies to boost
integration capacity has been developed [
19
]. The
identification of minimal requirements, essential to connect
existing and future study (meta) databases, facilitates
data exchange and data interpretation, helping to
increase the robustness of results from future joint data
analysis in nutritional epidemiology [
31
]. In fact, joint
data analysis has already started helping to achieve new
discoveries [
32
]. In the ONS, we have included the
minimal required study information in the growing
conceptual/ontological framework. Each minimal required
study term was placed at the appropriate hierarchical
level in the ontology. To easily identify terms pertaining
to the minimal study information, an annotation
property (“in_minimal_requirements_subset”) was created.
Application scenarios
The ONS is designed to enable the description of both
intervention and observational studies in human
nutrition. Here, we present two application scenarios based
on published nutritional studies, one for the
observational study design and one for the interventional study
design. Figures 2 and 3 illustrate how the ONS was built
to support the standardized annotation of most
descriptors of a nutritional study, starting from initial phases of
a study (i.e., formalizing the definition of population
stratum) to finally connect to the specific results and
how they were obtained. Figures and descriptions have
to be intended at the single instance level (i.e., specific
for the study object of description). For this reason, we
introduced the use of individuals (and their connections)
for very study-specific element alongside concepts in
classes. In the text below, the italic notation indicates
the properties, while the notation PREFIX:CLASS is
used to indicate classes in the ontology, for example the
notation “ONS:Diet” indicates the class with label “Diet”
in the ONS ontology. For abbreviation of the ontologies,
we refer the reader to the list of imported ontologies in
the “Methods” section.
Observational studies
The first application scenario is represented by the
CHANCE study [
33
]. Figure 2 illustrates how the ONS
can be used to formalize information on how the study
was conducted. This observational study aims at
developing novel and affordable nutritious foods to optimize the
diet and reduce the risk of diet-related diseases among
groups at risk of poverty (ROP). The CHANCE study uses
two different approaches to draw its final conclusion. The
first is a literature search process (EDAM:Literature
search), performed with a specific textual literature
database query (i.e., an instance of the class ONS:Literature
database query). Output of the literature search process is
a number of scientific publications (IAO:Scientific
publication) which are subject to analysis and review to extract
data (OBCS:data collection from literature), a process that
ultimately results in an organized data matrix (OBCS:Data
matrix). CHANCE also included an observational study
approach. In this case, a population was firstly divided into
sub-populations based on their economic income. This
stratification (STATO:Population stratification prior to
sampling) was carried out following a specific stratification
rule (STATO:Stratification rule), based on the risk of
poverty (ROP) of the subjects assessed with a questionnaire
(ONS:Income assessment). The stratified population was
then challenged with (i.e., is specified input of ) two
nutritional questionnaires (ONS:Food frequency and ONS:
Food diary) aimed at assessing the foods consumed by the
subjects and producing results finally organized in a data
matrix. In both cases, the data matrices (OBCS:Data
matrix) specific for this study contain information about
the nutrients and food consumed by the population and
represent the specified data object on which conclusions
are drawn (OBI:drawing a conclusion based on data).
Intervention studies
The second application scenario is represented by the
FLAVURS (impact of increasing doses of flavonoid-rich
and flavonoid-poor fruit and vegetables on
cardiovascular risk factors in an ‘at risk’ group) study [
34
]. Figure 3
illustrates how the ONS can be used to formalize the
information on how the study was conducted. This
interventional study aimed to investigate the effects of high
and low flavonoid diets on the vascular function and
other cardiovascular disease risk factors. In this study, a
population, selected on the basis of the stratification rule
(STATO:Stratification rule) of having a relative risk of
developing cardiovascular disease higher than 1.5, has
been randomly divided (OBI:Group randomization and
OBI:Randomized group participant role) into three
groups: control group (CT), high flavonoid group (HF),
and low flavonoid group (LF). Each of the groups was
challenged with a different diet (ONS:Diet): CT followed
the usual diet (ONS:Usual Diet), which is defined to
have exactly 0 interventions (ERO:Intervention); in the
HF and the LF groups, individuals were challenged with
two different types of intervention diet
(ONS:Intervention diet) encompassing two different intervention
(ERO:Intervention) protocols. In HF diet, the
intervention was performed by the prescription of consuming
fruit and vegetables with high flavonoid content, while
in the LF diet the intervention was concretized by the
prescription of consuming fruit and vegetables with low
flavonoid content.
Urine and blood (OBI:Urine specimen and OBI:Blood
specimen) were collected from individuals
(OBI:Collecting specimen from organism) and analyzed (i.e., they
inherited the evaluant role OBI:Evaluant role) by an
HPLC assay (HPLC class) including untargeted
metabolomics [
35
]. Output of the analysis was a data item in
the form of a matrix (OBCS:Transformed data item) that
is used to draw specific FLAVURS conclusions (OBI:
Drawing a conclusion based on data and OBI:conclusion
based on data).
Discussion and conclusions
The ONS is the first systematic effort to provide a
formal ontology framework for the description of
nutritional studies. In this context, the main aim of the ONS
is the establishment of an ontological framework that
can assist nutrition researchers by selecting the
appropriate terms from the wide range of existing ontologies
and creating the relevant missing key concepts for the
field. Nutrition researchers, who might not necessarily
be familiar with ontologies and concept standardization,
can find in the ONS a single knowledge entry point for a
unified and standardized terminology without having to
resort to numerous ontology sources. In addition to
standardizing concept descriptions and assisting in
annotation, the ONS will structure querying of nutritional
studies stored in public databases (such as the resources
developed in the ENPADASI project). Finding the
suitable studies (i.e., those more directly comparable
regarding design, employed stratification criteria, or type
of intervention diet employed) represents the basis for
integrated analysis. Such a query, in fact, cannot be
efficiently based on string matching, but rather on more
complex textual analysis and machine learning
methodologies for which ontology is crucial. A well-established
nutritional ontology would also enable more accurate
search for required data as well as the automated
integration and analysis of data from multiple sources [
36
].
Diet, nutrient, and food are indeed central concepts
for nutritional sciences, and they were included and
connected with higher level concepts in ONS. Moreover, the
ONS supports the research needs identified by other
initiatives such as the Food Biomarkers Alliance (FoodBAll)
by including for the first time in a formal ontology the
concept of biomarker in nutrition, and its sub-classes, as
defined in [
30
].
Besides acquiring widespread utilization, an ontology
can be considered successful only if (i) continuous
development and (ii) constant contribution/updates from
researchers with specific knowledge is ensured. We invite
and encourage researchers in the nutritional field to
contribute to the further development, adoption, and
promotion of the ONS. Contributions are already possible
using the GitHub tracking/issues system (Additional file 1)
and an online community platform to facilitate the
process of curation and extension of the ONS will be
developed for this purpose. As a next challenge, the ONS
aims to integrate nutritional studies with non-life sciences
such as economy, psychology, and sociology, which also
influence the nutritional status of individuals [
37–39
].
Additional file
Additional file 1: Agile introduction on ontologies, how to use them
and contribute to ONS. (PDF 2119 kb)
Abbreviations
DASH-IN: Data Sharing In Nutrition; EFSA: European Food Safety Authority;
ENPADASI: European Nutritional Phenotype Assessment and Data Sharing
Initiative; FLAVURS: Impact of increasing doses of flavonoid-rich and
flavonoid-poor fruit and vegetables on cardiovascular risk factors in an “at
risk” group; FoodEx2: Version 2 of the EFSA Food classification and
description system for exposure assessment; HPLC: High-performance liquid
chromatography; MeSH: Medical Subject Headings; OBO: Open Biomedical
Ontologies; ONS: Ontology for Nutritional Studies; OWL: Web Ontology
Language; RDF: Resource Description Framework; ROP: Risk of poverty
Funding
This work was supported by the Italian Ministry of University and Research
(MIUR) [decreto n. 2224, 05/10/2015 and n.2027, 11/09/2015] and by the
European Nutritional Phenotype Assessment and Data Sharing Initiative
(ENPADASI) and its infrastructure as part of the Joint Programming Initiative
“A Healthy Diet for a Healthy Life” (JPI-HDHL).
Availability of data and materials
The ONS ontology is freely available in OWL format under the Creative
Commons license (CC BY 4.0) from GitHub (https://github.com/enpadasi/
Ontology-for-Nutritional-Studies) and NCBO BioPortal (http://
bioportal.bioontology.org/ontologies/ONS). GitHub repository reports a
general guide on ontologies, as well as instruction on how to browse,
download, and contribute to ONS (available at https://github.com/enpadasi/
Ontology-for-Nutritional-Studies/wiki). ONS is also published in the
FAIRsharing database (https://fairsharing.org/bsg-s001068).
Authors’ contributions
FV, RL, and DC led the content creation, ontology development, and
manuscript drafting. MP curated the link between the ONS and the minimal
study requirements. DR, FM, PF, AB, AT, FC, GF, FT, FM, NDC, CL, BDB, GDT,
MP, KN, TP, JB, and DC contributed to the curation of the ONS-specific terms
and critically commented the manuscript. DC coordinated the ontology
development and manuscript drafting. All other authors critically commented
the manuscript. All authors read and approved the final manuscript.
Ethics approval and consent to participate
Not applicable
Competing interests
The authors declare that they have no competing interests.
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
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